I am a newbie to data science and ML. I am working on a classification problem where the task is to predict loan status (granted/not granted).
I am running a logistic regression model on the data. The accuracy of my model is 82%. However, my model is more sensitive (sensitivity = 97%) and less specific(specificity = 53%).
I want to increase the model's specificity. At this stage, after referring to a bunch of internet resources, I am confused about how to proceed.
Below is my observation : In Testing data,
a percentage of 1's in the class label is 73.17073170731707.
Testing data has more 1's than 0's in the class label. Is this the reason behind model being highly sensitive.
I am attaching my data file and code file. Please take a look at it.
Data sample :
Process : Data --> missing value imputation -->distribution analysis-->log transformation for normal distribution ---> one hot encoding --> feature selection --> splitting data --> model selection and evaluation
Code snippets :
Here I have selected "3 best features": Credit History, Property Area
How should I proceed? Any help (even if it's just a kick in the right direction) would be appreciated.